Method of monitoring and/or controlling a semiconductor manufacturing apparatus and a therefor

ABSTRACT

A method and system are provided for controlling and/or monitoring a semiconductor processing apparatus while predicting its processing results. The system includes a sensor for monitoring a processing state of the processing apparatus, a sensed data storage unit for preserving sensed data sent from the sensor, an input device for inputting measured values for processing results of semiconductor devices processed by the processing apparatus, a processing result measured value storage unit for preserving the inputted processing result measured values, a model equation generation unit for generating a model equation from preserved sensed data and processing result measured values, a model equation storage unit for preserving the generated model equation, a model equation based prediction unit for predicting processing results from the preserved model equation and the sensed data, and a process recipe control unit for controlling processing conditions of the processing apparatus from predicted processing results.

CROSS REFERENCE TO RELATED APPLICATION

This is a divisional of U.S. application Ser. No. 09/946,732, filed Sep.6, 2001, the subject matter of which is incorporated by referenceherein.

BACKGROUND OF THE INVENTION

The present invention relates to a semiconductor processing apparatus,and more particularly, to a semiconductor processing apparatus whichpredicts processing results to improve the operating rate andreliability of the apparatus and a method of monitoring and/orcontrolling the semiconductor processing apparatus.

In recent years, the dimensions of semiconductor devices have beenminiaturized more and more, so that a severe manufacturing dimensionaccuracy is required to such an extent that a gate electrode of 0.1 μmor smaller should be processed in a dimensional accuracy of 10% or less.On the other hand, in a semiconductor manufacturing apparatus forprocessing a semiconductor wafer using heat and plasma and reactionproducts, resulting from chemical reactions within the apparatus, areattached and remain on inner walls of the apparatus. Such reactionproducts change a wafer processing state in the apparatus over time. Forthis reason, as a number of wafers are sequentially processed by thesemiconductor manufacturing apparatus, the shape of semiconductordevices on wafers gradually changes to cause deteriorated performance.To accommodate this problem, generally, various countermeasures havebeen taken. For example, the inner walls of the chamber are cleanedusing plasma to remove products attached thereon, or the walls of thechamber are heated so that products are less likely to adhere on theinner walls. However, in most cases, such countermeasures are notperfect, inevitably resulting in a gradual change in the shape ofprocessed semiconductor devices. For this reason, the manufacturingapparatus must undergo replacement of parts and wet cleaning before theshape of processed devices changes so as to cause a problem. Inaddition, fluctuations in a variety of states of the apparatus involvein variations in the shape of devices processed on wafers, other thandeposited films. To address these problems, there have been createdtechniques for detecting a change in a processing state within asemiconductor manufacturing apparatus and feeding back the result ofdetection to the input of the semiconductor manufacturing apparatus tomaintain the processing state constant.

Such a method of monitoring fluctuations in plasma processing isdisclosed, for example, in JP-A-10-125660. This official document showsa method of predicting the performance of an apparatus and diagnosingthe state of plasma using an equation representing the relationshipbetween plasma processing characteristics and electric signals generatedin the apparatus. Specifically, JP-A-10-125660 discloses a method ofderiving an approximate expression which represents the relationshipbetween three electric signals and the plasma processing characteristicsof the apparatus through multiple regression. Another example isdisclosed in JP-A-11-87323. A method disclosed in JP-A-11-87323 adapts ageneral detection system having a multiplicity of existing detectorsmounted thereon to a semiconductor manufacturing apparatus to monitorthe state of the apparatus from a correlation signal of signals detectedby the detectors. Specifically, the correlation signal is generated by acalculation based on the ratio of six electric signals. A furtherexample is disclosed in U.S. Pat. No. 5,658,423. This U.S. patentdiscloses a method of monitoring the state of an apparatus by capturinga number of signals from a light and a mass analyzer to generate acorrelation signal for monitoring. The correlation signal is generatedusing a principal component analysis.

SUMMARY OF THE INVENTION

However, the method disclosed in JP-A-10-125660 fails to perform asuccessful prediction using the multiple regression when there are alarge number of sensor data for monitoring the apparatus sinceexplanatory variables include a large number of signals which are notrelated to the processing performance intended for the prediction. Themethod disclosed in JP-A-11-87323 in turn is a general method whichperforms the diagnosis using a signal correlated to a multiplicity ofdetected signals from a multiplicity of known detecting means, whereinthe correlation is established by taking the ratio of several signals,just as conventional approaches. This method, therefore, wouldencounters difficulties in applying to a specific system for accuratelymonitoring the state of a semiconductor manufacturing apparatus whichcan take a variety of states depending on a large number of causes forfluctuations. Unlike the foregoing methods, U.S. Pat. No. 5,658,423discloses a method of monitoring the state of plasma by analyzing aprincipal component of a large amount of data monitored from anapparatus to capture fluctuations in the state of the apparatus.However, semiconductor manufacturing apparatus for use in actual massproduction would not work well only with a concept of adapting a generalstatistic processing method as disclosed. For example, it is unknown inmost cases how a change in the principal component will cause what kindof result in the processing.

It is an object of the present invention to provide a semiconductorprocessing apparatus and method which monitor a processing state todetect faulty processing or predict processing results based on amonitored output to improve the operating rate and reliability of thesemiconductor processing apparatus for processing a variety of types ofdevices.

According to an aspect of the present invention, a semiconductor deviceprocessing apparatus includes a sensor for monitoring a processing stateof the semiconductor processing apparatus, processing result input meansfor inputting measured values for processing results of a semiconductorwafer processed by the semiconductor processing apparatus, a modelequation generation unit relying on sensed data acquired by the sensorand the measured values to generate a model equation for predicting aprocessing result using the sensed data as an explanatory variable, aprocessing result prediction unit for predicting a processing resultbased on the model equation and the sensed data, and a process recipecontrol unit for comparing the predicted processing result with apreviously set value to control a processing condition of thesemiconductor processing apparatus such that a deviation between thepredicted processing result and the previously set value is corrected.

With the foregoing configuration, according to the present invention,monitored data is acquired by the sensor from the semiconductorprocessing apparatus to generate a model equation which is used topredict processing results before measuring processing results ofsamples or without measuring the processing results, thereby improvingthe operating rate and reliability of the semiconductor processingapparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of a system for controlling a semiconductordevice processing apparatus illustrating one embodiment of the presentinvention;

FIG. 1B is a block diagram illustrating an exemplary modification to thecontrol system of FIG. 1A;

FIG. 2 is a block diagram illustrating another embodiment of aprocessing state monitoring unit in FIG. 1;

FIG. 3 is a block diagram illustrating an embodiment of secondaryprocessing state monitoring unit;

FIG. 4 is a flow chart illustrating an exemplary method of monitoring aprocessing state of a device;

FIG. 5A is a flow chart for explaining the operation of a modelgeneration unit;

FIG. 5B is a table illustrating a method of predicting measured valuesfor processing results of n wafers;

FIG. 6 is a graph for explaining a robust regression analysis;

FIG. 7 is a flow chart illustrating a routine or creating a modelequation;

FIG. 8 is a table for explaining a correction operation of a processrecipe control unit;

FIG. 9 is a flow chart for explaining the operation of the processingcondition generation unit; and

FIG. 10 is a diagram for explaining directions of control for processingconditions.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following, one embodiment of the present invention will bedescribed with reference to the accompanying drawings.

FIG. 1A illustrates a first embodiment of the present invention. In FIG.1A, a processing apparatus 1 is equipped with a processing statemonitoring unit 2. The processing state monitoring unit 2 may beincorporated in the apparatus 1, or installed external to the apparatus1. Alternatively, as illustrated in FIG. 1B, the processing statemonitoring unit 2 may be installed at a remote location through anetwork or the like. Further alternatively, as illustrated in FIG. 1B, aportion of its functions may be separated through a network or the like.Specifically, the processing state monitoring unit 2 has the followingconfiguration. First, the processing state monitoring unit 2 has asensor unit 3 for monitoring a processing state in the processingapparatus 1 involved in the processing of a wafer. The sensor unit 3 isgenerally comprised of several types of sensors. For example, for plasmaprocessing in a plasma etching apparatus, a plasma CVD apparatus and soon, the sensor unit 3 acquires the intensity of emitted light at eachwavelength in a plasma during processing, spectrally resolved using aspectrometer. For example, when using a spectrometer having 1,000channels of CCD array, 1,000 sets of sensed data can be acquired in eachsampling. In addition, the pressure, temperature, gas flow rate, and soon of the apparatus may also be used as sensed data. Also, results ofelectrical measurements made on electric current, voltage, impedance,and harmonic components thereof may be used as sensed data. During theprocessing of a wafer, these sensed data are acquired at proper timeintervals. The acquired sensed data are preserved in a sensed datastorage unit 4. A processed wafer undergoes measurements of processingresults using a processing result measuring device external to orincorporated in the apparatus 1. The measurements of processing resultsmay involve a measurement of CMOS gate width using CDSEM; a measurementof a processed shape such as a cross-sectional shape using across-section SEM; or a measurement of electrical characteristics of aprocessed device. Generally, however, such processing need not beperformed on all wafers, but generally, some of wafers may only beextracted for the measurements of the processing results. The processingstate monitoring unit 2 has a processing result measured value inputunit 5 for receiving measured values of the processing results. Theinput unit 5 may be a reader for reading information recorded on aportable medium such as a floppy disk, CD-ROM and so on, or a wired or awireless network connection device. The measured values of processingresults received from the input device 5 are preserved in a processingresult measured value storage unit 6. The storage unit 6 preservesmeasured values of processing results for each of a variety of devices.A model equation generation unit 7 fetches, from the sensed data storageunit 4 and the processing result measured value storage unit 6, thosesamples of the same types of devices, for which both sensed data andmeasured values of processing results have been preserved therein. Whenthe number of samples is, for example, three or more, the model equationgeneration unit 7 generates a model equation for predicting measuredvalues of processing results using the sensed data as explanatoryvariables. In this event, generally, it is difficult to automaticallyselect sensed data for use in the prediction due to a large number and avariety of types of sensed data acquired in the measurements.Particularly, when a variety of devices are processed, data effectivefor the prediction differs from one device to another, causing itdifficult to previously determine sensors for use in the prediction.

FIG. 5A is a diagram for explaining model equation generation processingbased on a PLS (Partial Least Square) method. As illustrated in FIG. 5A,the PLS method automatically generates from a large number of senseddata an explanatory variable which has the highest correlation tofluctuations in data to be predicted. Simultaneously, a function forcalculating the explanatory variable can be derived from the senseddata. Assume for example that measured values for processing results ofn wafers are intended for the prediction, and Yi represents a measuredvalue for the processing result of an i-th wafer. When m sensed data areacquired from one wafer, Sij indicates a j-th sensed data on the i-thwafer. The m sensed data may be data taken at different times from thesame sensor or taken at the same time from the different sensors. FIG.5B is a diagram for explaining the sensed data Sij. As shown in FIG. 5B,when the processing performed by the apparatus 1 on a single wafer isdivided into three steps with different processing conditions, and threesensors A, B, C are used, Sij may be taken in a range of S11 to Sn9 asshown. Sij may be an average value of sensed data in each step of theprocessing, or in some cases, may be rather converted values from senseddata such as a squared or an inverted version of the sensed data. Withthe use of the PLS method, the sensed data Sij can be converted into mexplanatory variables Xik which is arranged in the order of themagnitude of correlation to fluctuations in the processing resultmeasured value Yi. A function Fk for converting the sensed data Sij intothe explanatory variables Xk is expressed by the following Equation (1):

Xik=Fk(Si1, Si2, . . . , Sim)  (1)

Some of the explanatory variable Xik are relied on to predict processingresult measured values. Generally, since an explanatory variable Xil hasthe highest correlation to a processing result measured value Yi, Xi1,Xi2, Xi3 and so on are selected as explanatory variables. In the PLSmethod, a prediction equation such as the following Equation (2) isgenerated simultaneously. However, it may be better case by case tocreate the prediction Equation (2) using explanatory variables such asXil mentioned above.

Yi=p(Xi1, Xi2, Xi3)  (2)

On the other hand, the processing result measured values may includedata of wafers which indicate bad wafer processing states and thereforeabnormal processing result measured values. A prediction performed usingnormal multiple regression for such data would result in generation of amodel equation which has a low prediction accuracy due to the influenceof abnormal data, as shown in FIG. 6. To avoid such low predictionaccuracy, robust regression may be used for the prediction. With the useof the robust regression, a correct prediction model equation can begenerated because abnormal data as shown in FIG. 6 are removed from dataintended for prediction as outlier.

FIG. 7 illustrates a flow chart for explaining the model equationcreation processing performed by the model equation generation unit 7.

When there are a large number of types of sensed data, the modelequation generation unit 7 analyzes principal components of the senseddata (steps 701, 702), and performs the robust regression using theresultant principal components to predict processing results (steps705-706). In this event, since explanatory variables include principalcomponents which are not required for the prediction of processingresults, so that principal components with a smallest regressioncoefficient is removed from the explanatory variable (step 707), onemore principal component is added to the explanatory variables (step704), and the multiple regression is again performed (step 706), asillustrated in the flow chart. This loop of processing is repeatedlyexecuted until a prediction error is reduced below a predetermined value(step 708). These regression analyses may be linear, or non-linearregression analysis may be used as derived from physical characteristicsand experimental values of the processing.

The model equation generated by the method as described above ispreserved in a model equation storage unit 8 in FIG. 1. Since the modelequation is generated for each of various types of devices, a number ofmodel equations equal to the number of types of devices processed by theprocessing apparatus 1 are preserved in the model equation storage 8.When a wafer of a certain device is loaded into the processing apparatus1 for processing, a model equation corresponding to this device isloaded into a prediction unit 9. Signals generated from the sensor unit3 during the processing of the device are converted to explanatoryvariables using the equation (1) derived from the PLS method, ifrequired, or converted to principal components through the principalcomponent analysis, and predicted values for processing results arecalculated by the model equation expressed by the equation (2). Thecalculated predicted values are passed to a process recipe control unit10 which changes processing conditions to correct a deviation of thepredicted values from set values for the processing results.

Next, a specific method will be described for correcting processingconditions in the process recipe control unit 10. Here, the PLS methodis used again. In normal processing of semiconductor devices, severalconflicting processing performances may often be required asrequirements for the processing. For example, etching of a gateelectrode or the like requires the verticality of side walls of the gateelectrode, and the etching selectivity of gate polysilicon to underlyinggate oxide film. Specifically, for achieving the verticality of the sidewalls, etching conditions with scare adhesive products should be used.For achieving high selectivity to the underlying oxide film, etchingconditions with plentiful adhesive products should be used. When thereare two conflicting conditions as mentioned, the processing conditionsare difficult to control. FIGS. 8 to 10 are diagrams for explainingprocessing conditions for satisfying such conflicting requirements.Assuming that an aging change of a processing apparatus 1 results in adegraded verticality of the side walls, even if a reduction in aprocessing condition 1 (here, a flow rate of a gas A), for example,improves the verticality, this reduction simultaneously causes lowerselectivity to the underlying oxide film, so that this is not preferableas a processing condition.

It is therefore necessary to find a combination of the processingcondition land a processing condition 2 (here, wafer bias power) toimprove the verticality of the side walls while preventing theselectivity to the underlying oxide film from degrading.

To solve this problem, as shown in FIG. 8, experimental conditions withdifferent processing conditions are set at several to several tens ofpoints around a central condition under which the processing is normallyperformed, and the processing is performed under these experimentalconditions to measure processing results. Points 1-4 in FIG. 10correspond to the experimental conditions 1-4 in FIG. 8. Assume hereinthat measured values for the verticality of the side walls are taken asprocessing result measured values A, and an underlying oxide filmselection ratio is taken as processing result measured values B.

As illustrated in FIG. 9, the PLS method is applied to this experiment,using a correlation of two processing conditions to two processingresult measured values, to derive a direction A in which the conditionshave the highest correlation to the verticality of the side walls, asshown in FIG. 10. A direction B orthogonal to the direction A, in whichthe conditions have the highest correlation to the selectivity to theunderlying oxide film is calculated from the direction in which theconditions have the highest correlation to the selectivity to theunderlying oxide film and which is derived by the PLS method like theabove. These condition direction A and condition direction B have beenset in the process recipe control unit 10 in FIG. 1A. With theseconditions set beforehand, when deteriorated verticality of the sidewalls is predicted in the prediction unit 9 based on the model equation,the processing condition may be modified in the condition direction A toimprove the verticality of the side walls without scarifying theunderlying oxide film selection ratio. The calculated condition controldirections are preserved in a process recipe control direction storageunit 14 for use in modifying associated processing conditions whenpredicted values for processing results calculated by the model equationdeviate from set values.

While the example described herein modifies two processing conditions,the PLS method can modify a larger number of processing conditions. Asmore processing conditions are modified, more preferable results can beprovided. Also, conflicting processing result measured values are notlimited to two, but a larger number of processing results can be takeninto account. For example, in addition to the verticality of the sidewalls and the selection ratio to the underlying oxide film, a maskselection ratio or the like may be employed.

FIG. 2 illustrates another embodiment of the present invention. Whilethe system of FIG. 2 is substantially identical in configuration to thesystem of FIG. 2, the former differs from the latter in that a predictedvalue display unit 11 is provided instead of the process recipe controlunit 10 to display a message for notifying a fault on a display of theapparatus or the like. The display unit 11 may be implemented by abuzzer for generating an alarm, posting an email, and so on.

FIG. 3 illustrates a further embodiment of the present invention. Thesystems so far described above rely on the prediction of processingresults using a model equation, so that they cannot monitor theprocessing of a device for which no model equation has been generated.Measurements of processing results often take a very long time, so thatmeasurements may be hardly made on all of the processing results. Thus,the model equation cannot be generated for such a device. To addressthis problem, a principal component extraction unit 12 extractsprincipal components from a variety of types of sensed data, and a faultmonitoring unit 13 monitors variations in the principal components todetect a fault in the processing, as illustrated in FIG. 3. If a faultis detected, the apparatus should stop the processing of the next wafer.The fault may be detected using, for example, a variation managementmethod called “SPC” (Statistical Process Control). To implement thismethod, averages and variances of principal components during theprocessing of an intended device are stored to determine that theprocessing is faulty if a measured principal component deviates from itsaverage by several times as much as its variance.

FIG. 4 is a flow chart for explaining a processing flow suitable for aprocessing apparatus which comprises the processing state monitoringunit 2 and a secondary processing state monitoring unit 2′. It is firstdetermined whether or not a model equation has been generated andpreserved for a device intended for processing (step 501). When themodel equation has been preserved, the processing state monitoring unit2 executes monitoring control (step 502). When no model equation hasbeen preserved, the secondary processing state monitoring unit 2′executes the monitoring control (step 503).

The provision of the secondary processing state monitoring unit 2′ usingonly principal components as in FIG. 3 as well as the processing statemonitoring unit 2 using the model equation as in FIGS. 1A and 2 willpermit the monitoring of processing states of either of a device forwhich a model equation has been generated and preserved as illustratedin FIG. 4 and a device for which no model equation has been generated.Such a monitoring system having flexibility is preferred for monitoringa semiconductor processing apparatus.

What is claimed is:
 1. A method of controlling processing of ansemiconductor object to be processed in a semiconductor processingapparatus using a sensor for monitoring a processing state of saidsemiconductor processing apparatus, comprising the steps of: inputtingmeasured values for processing results of a semiconductor objectprocessed by said semiconductor processing apparatus to hold the inputmeasured values: based on sensed data acquired by said sensor and saidheld measured values, generating a model equation for predicting aprocessing result using said sensed data as an explanatory variable;predicting a processing result of the semiconductor object based on saidmodel equation and said sensed data; and comparing said predictedprocessing result with a pre-set value and controlling a processingcondition of said semiconductor processing apparatus based on thecomparison result so as to connect an error between said predictedprocessing result and said preset value, wherein said model equationgeneration step generate a model equation using a PLS method (PartialLeast Square method).
 2. A method of controlling processing of ansemiconductor object to be processed in a semiconductor processingapparatus using a sensor for monitoring a processing state of saidsemiconductor processing apparatus, comprising the steps of: inputtingmeasured values for processing results of a semiconductor objectprocessed by said semiconductor processing apparatus to hold the inputmeasured values: based on sensed data acquired by said sensor and saidheld measured values, generating a model equation for predicting aprocessing result using said sensed data as an explanatory variable;predicting a processing result of the semiconductor object based on saidmodel equation and said sensed data; and comparing said predictedprocessing result with a pre-set value and controlling a processingcondition of said semiconductor processing apparatus based on thecomparison result so as to connect an error between said predictedprocessing result and said preset value, wherein said model equationgeneration step generates a model equation using robust regression.
 3. Amethod of controlling processing of an semiconductor object to beprocessed in a semiconductor processing apparatus using a sensor formonitoring a processing state of said semiconductor processingapparatus, comprising the steps of: inputting measured values forprocessing results of a semiconductor object processed by saidsemiconductor processing apparatus to hold the input measured values:based on sensed data acquired by said sensor and said held measuredvalues, generating a model equation for predicting a processing resultusing said sensed data as an explanatory variable; predicting aprocessing result of the semiconductor object based on said modelequation and said sensed data; and comparing said predicted processingresult with a pre-set value and controlling a processing condition ofsaid semiconductor processing apparatus based on the comparison resultso as to connect an error between said predicted processing result andsaid preset value, wherein said model equation generation step generatesa model equation using principal component robust regression.
 4. Asemiconductor processing monitoring method for monitoring processing ofa semiconductor object to be processed in a semiconductor processingapparatus using a sensor for monitoring a processing state of saidsemiconductor processing apparatus, comprising the steps of: inputtingmeasured values for processing results of a semiconductor objectprocessed by said semiconductor processing apparatus; based on senseddata acquired by said sensor and said measured values, generating amodel equation for predicting a processing result using said sensed dataas an explanatory variable; predicting a processing result of thesemiconductor object based on said model equation and said sensed data;and displaying said predicted value or a deviation of said predictedvalue from a pre-set value.
 5. A semiconductor processing monitoringmethod for monitoring processing of a semiconductor wafer in asemiconductor processing apparatus using a multiplicity of sensors formonitoring processing states of said semiconductor processing apparatus,comprising the steps of: extracting a principal component based on amultiplicity of sensed data acquired by said multiplicity of sensors;and detecting a fault in processing based on variations in the principalcomponent extracted by said extraction step.
 6. A semiconductorprocessing controlling method according to claim 1, further comprisingthe steps of: extracting a principal component based on sensed dataacquired by said sensor; detecting a fault in processing based onvariations in the principal component extracted by said extraction step,and stopping the processing operation of said semiconductor processingapparatus when said fault detection unit detects a fault if said modelequation generation step has not generated a model equation.
 7. Asemiconductor processing controlling method according to claim 1,further comprising: preserving said sensed data in a sensed data storageunit; and preserving a processing result inputted by said processingresult input step in a processing result measured value storage unit,wherein said model equation generation step generates said modelequation based on the sensed data and the measured value preserved inthe respective storage units associated therewith, and preserves thegenerated model equation in a model equation storage unit.